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Archive of posts filed under the Stan category.

The Stan Core Roadmap

Here’s the plan for Stan core development that Bob presented at Stancon last week (that is, back at the end of August, 2018): Part I. Rear-View Mirror Stan 2.18 Released Multi-core Processing has Landed! Multi-Process Parallelism Map Function New Built-in Functions Manuals to HTML Improved Effective Sample Size Foreach Loops Data-qualified Arguments Bug Fixes and […]

Stan This Month

So much is going on with Stan that it can be hard to keep track, so we (the Stan project) are starting a monthly update and newsletter.  If you want to be included in the monthly mailing list, just type in your email here. Charles Margossian is the editor of Stan This Month and indeed […]

Principal Stratification on a Latent Variable (fitting a multilevel model using Stan)

Adam Sales points to this article with John Pane on principal stratification on a latent variable, and writes: Besides the fact that the paper uses Stan, and it’s about principal stratification, which you just blogged about, I thought you might like it because of its central methodological contribution. We had been trying to use computer […]

Autodiff! (for the C++ jockeys in the audience)

Here’s a cool thread from Bob Carpenter on the Stan forums, “A new continuation-based autodiff by refactoring,” on the inner workings of Stan. Enjoy. P.S. Just for laffs, see this post from 2010 which is the earliest mention of autodiff I could find on the blog.

Transforming parameters in a simple time-series model; debugging the Jacobian

So. This one is pretty simple. But the general idea could be useful to some of you. So here goes. We were fitting a model with an autocorrelation parameter, rho, which was constrained to be between 0 and 1. The model looks like this: eta_t ~ normal(rho*eta_{t-1}, sigma_res), for t = 2, 3, … T […]

NYC Meetup Thursday: Under the hood: Stan’s library, language, and algorithms

I (Bob, not Andrew!) will be doing a meetup talk this coming Thursday in New York City. Here’s the link with registration and location and time details (summary: pizza unboxing at 6:30 pm in SoHo): Bayesian Data Analysis Meetup: Under the hood: Stan’s library, language, and algorithms After summarizing what Stan does, this talk will […]

Reproducibility and Stan

Aki prepared these slides which cover a series of topics, starting with notebooks, open code, and reproducibility of code in R and Stan; then simulation-based calibration of algorithms; then model averaging and prediction. Lots to think about here: there are many aspects to reproducible analysis and computation in statistics.

Prior distributions for covariance matrices

Someone sent me a question regarding the inverse-Wishart prior distribution for covariance matrix, as it is the default in some software he was using. Inverse-Wishart does not make sense for prior distribution; it has problems because the shape and scale are tangled. See this paper, “Visualizing Distributions of Covariance Matrices,” by Tomoki Tokuda, Ben Goodrich, […]

StanCon 2018 Helsinki talk slides, notebooks and code online

StanCon 2018 Helsinki talk slides, notebooks and code have been available for some time in StanCon talks repository, but it seems we forgot to announce this. The StanCon 2018 Helsinki talk list includes also links to videos. StanCon’s version of conference proceedings is a collection of contributed talks based on interactive notebooks. Every submission is […]

Multilevel models for multiple comparisons! Varying treatment effects!

Mark White writes: I have a question regarding using multilevel models for multiple comparisons, per your 2012 paper and many blog posts. I am in a situation where I do randomized experiments, and I have a lot of additional demographic information about people, as well. For the moment, let us just assume that all of […]

Another Stan related job in baseball!

This quick post is for any Stan users out there who are interested in working in baseball. The Los Angeles Angels are looking to hire a Director of Quantitative Analysis and they are particularly interested in candidates with experience fitting models with Stan. If you are interested please see the full job posting.

Fitting the Besag, York, and Mollie spatial autoregression model with discrete data

Rudy Banerjee writes: I am trying to use the Besag, York & Mollie 1991 (BYM) model to study the sociology of crime and space/time plays a vital role. Since many of the variables and parameters are discrete in nature is it possible to develop a BYM that uses an Integer Auto-regressive (INAR) process instead of […]

Stan development in RStudio

Check this out! RStudio now has special features for Stan: – Improved, context-aware autocompletion for Stan files and chunks – A document outline, which allows for easy navigation between Stan code blocks – Inline diagnostics, which help to find issues while you develop your Stan model – The ability to interrupt Stan parallel workers launched […]

Stan on the web! (thanks to RStudio)

This is big news. Thanks to RStudio, you can now run Stan effortlessly on the web. So you can get started on Stan without any investment in set-up time, no need to install C++ on your computer, etc. As Ben Goodrich writes, “RStudio Cloud is particularly useful for Stan tutorials where a lot of time […]

Cool postdoc position in Arizona on forestry forecasting using tree ring models!

Margaret Evans sends in this cool job ad: Two-Year Post Doctoral Fellowship in Forest Ecological Forecasting, Data Assimilation A post-doctoral fellowship is available in the Laboratory of Tree-Ring Research (University of Arizona) to work on an NSF Macrosystems Biology-funded project assimilating together tree-ring and forest inventory data to analyze patterns and drivers of forest productivity […]

Postdoc position: Stan and composite mechanistic and data-driven models of cellular metabolism

Very cool project and possibility to work 3 years developing Stan and collaborating with me (Aki) and other Stan development team. Deadline for applications is 22 October. Quantitative Modelling of Cell Metabolism (QMCM) group headed by Professor Lars Keld Nielsen at DTU, Copenhagen, is looking for experienced Bayesian statistician for a postdoc position. Group specializes […]

Using Stacking to Average Bayesian Predictive Distributions (with Discussion)

I’ve posted on this paper (by Yuling Yao, Aki Vehtari, Daniel Simpson, and myself) before, but now the final version has been published, along with a bunch of interesting discussions and our rejoinder. This has been an important project for me, as it answers a question that’s been bugging me for over 20 years (since […]

The hot hand—in darts!

Roland Langrock writes: Since on your blog you’ve regularly been discussing hot hand literature – which we closely followed – I’m writing to share with you a new working paper we wrote on a potential hot hand pattern in professional darts. We use state-space models in which a continuous-valued latent “hotness” variable, modeled as an […]

“Dynamically Rescaled Hamiltonian Monte Carlo for Bayesian Hierarchical Models”

Aki points us to this paper by Tore Selland Kleppe, which begins: Dynamically rescaled Hamiltonian Monte Carlo (DRHMC) is introduced as a computationally fast and easily implemented method for performing full Bayesian analysis in hierarchical statistical models. The method relies on introducing a modified parameterisation so that the re-parameterised target distribution has close to constant […]

Against Winner-Take-All Attribution

This is the anti-Wolfram. I did not design or write the Stan language. I’m a user of Stan. Lots of people designed and wrote Stan, most notably Bob Carpenter (designed the language and implemented lots of the algorithms), Matt Hoffman (came up with the Nuts algorithm), and Daniel Lee (put together lots of the internals […]